Updated: 2020-08-08 07:35:10 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
Rhode Island 1.32 17885 124
Vermont 1.21 1450 5
South Dakota 1.18 9202 93
Kansas 1.16 30767 470
Illinois 1.14 191030 1796
Idaho 1.12 23993 516
Arkansas 1.11 46273 825
Indiana 1.10 73832 915
Virginia 1.10 77709 884
North Dakota 1.09 7294 130
Texas 1.09 497515 8874
Kentucky 1.06 35231 648
Montana 1.05 4700 121
Iowa 1.04 48009 489
Louisiana 1.04 129044 1903
Utah 1.04 43488 483
Nebraska 1.03 27967 293
West Virginia 1.03 7449 128
Wisconsin 1.03 59116 879
Delaware 1.02 15214 94
Georgia 1.02 190723 3314
Massachusetts 1.01 120033 393
New York 1.01 424352 667
Minnesota 1.00 59207 699
Oregon 0.99 20658 318
Washington 0.98 63993 752
Michigan 0.97 95383 703
New Hampshire 0.97 6783 28
Alabama 0.96 98383 1533
Colorado 0.96 50102 476
Missouri 0.96 51480 1046
Nevada 0.95 54863 947
Ohio 0.95 98814 1166
South Carolina 0.95 98357 1351
Wyoming 0.95 3003 41
North Carolina 0.94 133260 1603
Tennessee 0.94 115843 1954
Pennsylvania 0.93 122174 794
Oklahoma 0.92 42332 861
Mississippi 0.91 65673 1051
Maryland 0.89 94622 775
New Jersey 0.89 185295 346
California 0.88 548345 6657
Florida 0.83 518285 6873
New Mexico 0.80 22007 200
Maine 0.79 4034 15
Arizona 0.75 185702 1554
Connecticut 0.66 50139 85

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

National \(R_e\)

There is also large variation in the distribution of \(R_e\) values. This shows how that distribution has changed over the last three weeks. As a reminder, for disease reduction, \(R_e\) needs to be sustained below 1.0.

Distribtion Trend

Distribution of \(R_e\) Values

Distribution of Baseline Control

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Clark WA 8 1 1.4 2035 440 40
Pierce WA 4 2 1.1 6083 710 113
Grant WA 9 3 1.3 1496 1580 36
Whitman WA 24 4 1.7 111 230 4
King WA 1 5 1.0 16294 750 156
Walla Walla WA 17 6 1.4 522 870 18
Snohomish WA 3 7 1.0 6134 780 63
Spokane WA 5 8 0.9 4167 840 66
Yakima WA 2 12 0.8 10806 4330 48
Benton WA 6 13 0.8 3851 1980 34
Franklin WA 7 14 0.8 3589 3960 28
OR
county ST case rank severity R_e cases cases/100k daily cases
Lincoln OR 11 1 1.7 415 870 4
Columbia OR 23 2 1.6 92 180 3
Malheur OR 6 3 1.2 753 2470 17
Multnomah OR 1 4 0.9 4744 590 61
Marion OR 3 5 1.0 2833 840 37
Umatilla OR 4 6 1.0 2242 2920 43
Yamhill OR 10 7 1.2 434 420 14
Jackson OR 9 9 1.1 452 210 15
Clackamas OR 5 10 1.1 1505 370 21
Washington OR 2 11 0.9 2996 510 36
Lane OR 8 14 1.0 564 150 10
Deschutes OR 7 18 0.8 580 320 9
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Del Norte CA 50 1 3.1 98 360 2
Los Angeles CA 1 2 1.0 203574 2020 2382
Humboldt CA 43 3 2.0 278 200 9
Riverside CA 2 4 1.0 40192 1690 442
San Diego CA 5 5 0.9 31731 960 373
Santa Clara CA 10 6 1.0 11572 600 213
Contra Costa CA 15 7 1.0 8683 770 152
Fresno CA 7 11 0.8 16853 1720 288
Orange CA 3 12 0.8 38982 1230 339
San Bernardino CA 4 13 0.7 35134 1650 398
Alameda CA 9 20 0.8 12307 750 124
Kern CA 6 28 0.5 22164 2510 272
San Joaquin CA 8 35 0.6 12331 1680 105

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.7 125403 2950 1093
Pima AZ 2 2 0.9 17484 1710 193
Yavapai AZ 10 3 1.0 1988 880 33
Yuma AZ 3 4 0.7 11479 5520 71
Apache AZ 7 5 0.9 3151 4410 19
Coconino AZ 8 6 0.9 3065 2190 16
Mohave AZ 6 7 0.8 3160 1530 29
Navajo AZ 5 8 0.8 5357 4930 19
Pinal AZ 4 10 0.4 8404 2000 38
Santa Cruz AZ 9 13 0.6 2652 5690 8
CO
county ST case rank severity R_e cases cases/100k daily cases
Elbert CO 30 1 1.8 104 410 3
Broomfield CO 15 2 1.6 452 680 9
Pueblo CO 13 3 1.5 669 410 12
Summit CO 16 4 1.7 338 1110 3
El Paso CO 4 5 1.1 5054 730 80
Boulder CO 7 6 1.1 2054 640 24
Adams CO 3 7 0.9 6378 1280 62
Weld CO 6 8 1.1 3670 1240 24
Denver CO 1 9 0.9 10122 1460 72
Arapahoe CO 2 12 0.8 7253 1140 52
Jefferson CO 5 13 0.9 4125 720 37
Larimer CO 9 14 1.0 1495 440 20
Douglas CO 8 19 0.8 1720 520 15
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 20400 1820 195
Utah UT 2 2 1.1 8536 1450 124
Washington UT 5 3 1.1 2471 1540 30
Cache UT 6 4 1.3 1903 1560 16
Box Elder UT 12 5 1.4 358 680 7
Wasatch UT 11 6 1.4 550 1800 4
Millard UT 13 7 1.5 130 1020 2
Weber UT 4 8 0.9 2732 1100 32
Davis UT 3 9 0.9 3167 930 35
Tooele UT 9 13 1.0 580 890 8
San Juan UT 8 14 0.9 648 4240 5
Summit UT 7 15 0.8 708 1750 3
NM
county ST case rank severity R_e cases cases/100k daily cases
Doña Ana NM 4 1 1.1 2396 1110 35
Lea NM 7 2 1.1 748 1070 21
Eddy NM 14 3 1.3 284 490 6
Chaves NM 12 4 1.1 425 650 14
Bernalillo NM 1 5 0.7 5080 750 45
Curry NM 10 6 0.9 533 1060 12
Valencia NM 11 7 1.0 431 570 8
Santa Fe NM 9 11 0.8 632 420 9
Sandoval NM 5 12 0.7 1129 800 7
San Juan NM 3 13 0.7 3036 2380 7
McKinley NM 2 14 0.6 4041 5550 8
Cibola NM 8 15 0.3 674 2500 8
Otero NM 6 17 0.6 1100 1670 3

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Union NJ 6 1 1.4 16800 3040 10
Gloucester NJ 16 2 1.2 3219 1110 24
Essex NJ 2 3 0.9 19919 2510 29
Cumberland NJ 15 4 1.0 3335 2170 16
Middlesex NJ 4 5 0.9 18055 2180 26
Bergen NJ 1 6 0.9 20956 2250 34
Passaic NJ 5 7 0.9 17754 3520 24
Monmouth NJ 8 8 0.9 10361 1660 30
Hudson NJ 3 9 0.9 19772 2960 19
Camden NJ 9 12 0.8 8558 1690 29
Ocean NJ 7 13 0.7 10641 1800 23
PA
county ST case rank severity R_e cases cases/100k daily cases
Northumberland PA 29 1 1.7 439 480 8
Union PA 39 2 1.6 221 490 12
Cambria PA 35 3 1.5 330 250 14
York PA 13 4 1.3 2473 560 33
Mercer PA 30 5 1.4 401 360 12
Philadelphia PA 1 6 0.9 31119 1980 121
Delaware PA 3 7 1.0 9194 1630 70
Allegheny PA 4 10 0.8 8732 710 98
Berks PA 7 14 1.0 5323 1280 25
Lancaster PA 6 15 0.9 5819 1080 39
Chester PA 8 16 0.9 5109 990 35
Bucks PA 5 22 0.9 7158 1140 34
Montgomery PA 2 25 0.8 10024 1220 36
Lehigh PA 9 31 0.8 4921 1360 15
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 0.9 12993 1570 157
Baltimore city MD 4 2 0.9 12283 2000 146
Prince George’s MD 1 3 0.9 23894 2640 140
Worcester MD 15 4 1.2 687 1330 19
Anne Arundel MD 5 5 0.9 7276 1280 68
Montgomery MD 2 6 0.8 18190 1750 82
Cecil MD 14 7 1.2 692 680 10
Howard MD 6 9 0.9 3791 1200 34
Charles MD 8 10 1.0 1990 1260 21
Harford MD 9 12 0.8 1938 770 24
Frederick MD 7 17 0.6 3055 1230 9
VA
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg VA 31 1 2.8 366 1190 14
Richmond VA 36 2 2.5 322 3630 1
Russell VA 70 3 2.0 100 360 8
Essex VA 71 4 2.2 91 820 3
Louisa VA 53 5 2.0 178 500 2
Scott VA 76 6 1.8 78 350 5
Prince George VA 30 7 1.7 366 970 7
Fairfax VA 1 8 1.2 16201 1420 82
Prince William VA 2 11 1.1 9287 2030 72
Chesterfield VA 5 12 1.1 4279 1260 51
Arlington VA 8 18 1.2 3036 1310 22
Norfolk city VA 7 19 1.0 3622 1470 73
Virginia Beach city VA 4 22 0.9 4793 1060 98
Loudoun VA 3 28 1.0 5182 1350 29
Newport News city VA 9 31 1.0 1787 990 30
Henrico VA 6 38 0.9 3800 1170 36
WV
county ST case rank severity R_e cases cases/100k daily cases
Boone WV 24 1 1.9 99 430 4
Grant WV 23 2 1.7 110 940 9
Wayne WV 11 3 1.7 200 490 3
Logan WV 12 4 1.4 197 580 12
Cabell WV 4 5 1.1 377 400 9
Mingo WV 16 6 1.1 162 650 6
Greenbrier WV 25 7 1.5 91 260 1
Berkeley WV 3 9 1.0 684 600 6
Kanawha WV 2 10 0.8 899 480 15
Harrison WV 9 11 1.0 214 310 4
Raleigh WV 8 12 0.9 220 290 7
Wood WV 7 19 0.9 244 290 2
Ohio WV 6 20 0.8 268 630 3
Monongalia WV 1 21 0.5 942 890 5
Jefferson WV 5 22 0.8 296 530 1
DE
county ST case rank severity R_e cases cases/100k daily cases
Sussex DE 2 1 1.3 5806 2640 30
New Castle DE 1 2 0.9 7142 1290 50
Kent DE 3 3 1.0 2266 1300 14

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Jefferson AL 1 1 1.1 12971 1970 239
Jackson AL 28 2 1.4 978 1880 34
Talladega AL 23 3 1.2 1219 1510 40
Mobile AL 2 4 0.9 9760 2350 193
Coosa AL 67 5 1.7 114 1050 3
Montgomery AL 3 6 1.0 6640 2930 76
Pike AL 37 7 1.3 728 2180 13
Marshall AL 8 8 1.0 3166 3330 41
Shelby AL 7 13 0.9 3409 1610 52
Tuscaloosa AL 5 16 0.9 4222 2050 49
Lee AL 9 18 1.0 2783 1750 33
Madison AL 4 22 0.8 5384 1510 73
Baldwin AL 6 23 0.8 3509 1690 60
MS
county ST case rank severity R_e cases cases/100k daily cases
Neshoba MS 12 1 1.7 1286 4380 18
Bolivar MS 14 2 1.4 1125 3450 42
Scott MS 19 3 1.6 992 3490 8
Harrison MS 3 4 1.2 2411 1190 61
Stone MS 78 5 1.6 177 960 6
Marshall MS 36 6 1.3 662 1850 22
DeSoto MS 2 7 1.1 3604 2050 72
Jackson MS 6 8 1.1 2248 1580 61
Washington MS 9 19 1.0 1610 3420 28
Forrest MS 8 23 0.9 1756 2330 28
Hinds MS 1 25 0.7 5562 2300 67
Jones MS 7 40 0.8 1865 2720 20
Madison MS 4 42 0.7 2406 2320 23
Rankin MS 5 47 0.6 2257 1490 24
LA
county ST case rank severity R_e cases cases/100k daily cases
Lafayette LA 4 1 1.6 7582 3160 226
St. Landry LA 15 2 1.4 2668 3200 92
West Feliciana LA 53 3 2.0 340 2210 3
Evangeline LA 32 4 1.5 861 2560 27
East Baton Rouge LA 2 5 1.0 11961 2690 182
LaSalle LA 58 6 1.5 280 1870 10
Jefferson LA 1 7 1.0 15150 3480 142
Tangipahoa LA 9 11 1.1 3389 2600 54
Ouachita LA 8 12 1.0 4778 3060 64
St. Tammany LA 7 18 1.0 5079 2010 68
Caddo LA 6 21 0.9 6631 2670 78
Orleans LA 3 34 0.8 10642 2730 54
Calcasieu LA 5 42 0.6 6818 3410 78

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Franklin FL 64 1 4.9 356 3030 78
Taylor FL 48 2 3.6 968 4380 165
Dixie FL 63 3 3.0 386 2350 32
Baker FL 55 4 2.4 564 2030 46
Gulf FL 56 5 2.3 547 3410 50
Washington FL 50 6 1.8 802 3260 51
Calhoun FL 62 7 2.0 390 2700 20
Miami-Dade FL 1 10 0.7 129800 4780 1508
Hillsborough FL 4 11 0.9 31972 2320 391
Duval FL 6 14 0.9 22845 2470 258
Broward FL 2 17 0.7 61117 3200 701
Palm Beach FL 3 18 0.8 36201 2500 404
Polk FL 9 20 0.9 13849 2070 189
Orange FL 5 23 0.8 31074 2350 282
Pinellas FL 7 27 0.8 17575 1830 170
Lee FL 8 37 0.8 16260 2260 122
GA
county ST case rank severity R_e cases cases/100k daily cases
Bleckley GA 133 1 2.2 178 1390 10
Jasper GA 136 2 1.9 150 1090 6
Cobb GA 4 3 1.2 13014 1750 285
Cherokee GA 13 4 1.3 3239 1340 84
DeKalb GA 3 5 1.1 13450 1810 208
Gwinnett GA 2 6 1.0 19209 2130 314
Fulton GA 1 7 1.0 19594 1920 311
Chatham GA 6 10 1.0 5544 1930 112
Hall GA 5 12 1.1 5941 3030 90
Clayton GA 7 26 1.0 4866 1750 72
Richmond GA 9 32 0.9 4134 2050 94
Muscogee GA 8 36 0.9 4586 2330 58

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Karnes TX 81 1 3.2 557 3620 75
Winkler TX 178 2 3.5 79 1010 3
Fort Bend TX 11 3 2.1 9024 1220 375
Medina TX 69 4 2.5 688 1390 25
La Salle TX 100 5 2.9 361 4870 1
Smith TX 29 6 1.9 2637 1170 71
Bee TX 63 7 1.9 823 2520 64
Cameron TX 7 8 1.4 17113 4060 962
Tarrant TX 4 16 1.2 32715 1620 728
Hidalgo TX 6 18 1.3 19394 2280 414
Harris TX 1 21 1.0 83792 1820 1528
El Paso TX 8 29 1.1 15887 1900 249
Travis TX 5 32 1.1 22526 1870 259
Nueces TX 9 35 1.0 13923 3860 282
Dallas TX 2 41 0.8 53483 2070 470
Bexar TX 3 82 0.5 42408 2200 254
OK
county ST case rank severity R_e cases cases/100k daily cases
Pittsburg OK 31 1 2.0 282 640 23
Tulsa OK 2 2 1.0 10144 1580 219
Oklahoma OK 1 3 0.9 10329 1320 209
McIntosh OK 40 4 1.4 174 880 6
Le Flore OK 29 5 1.2 316 630 17
Washington OK 11 6 1.3 614 1180 9
Rogers OK 6 7 1.0 935 1030 27
Cleveland OK 3 8 0.8 2976 1080 58
Wagoner OK 8 9 1.0 828 1060 20
Canadian OK 4 15 0.8 1182 860 23
Texas OK 5 17 1.1 1049 4970 3
McCurtain OK 7 41 0.6 847 2570 4
Comanche OK 9 44 0.5 804 660 6

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Alpena MI 44 1 2.8 128 450 0
Bay MI 21 2 1.7 595 570 10
Macomb MI 3 3 1.1 10420 1200 115
Crawford MI 47 4 1.8 106 770 2
Menominee MI 45 5 1.5 125 540 6
Oakland MI 2 6 1.0 15278 1220 102
Wayne MI 1 7 0.9 28006 1590 140
Kent MI 4 9 0.9 7456 1160 55
Saginaw MI 8 11 1.1 1953 1010 20
Washtenaw MI 6 13 1.0 3025 830 22
Genesee MI 5 17 0.9 3634 890 26
Ottawa MI 9 25 0.9 1810 640 14
Jackson MI 7 47 0.4 2434 1530 5
WI
county ST case rank severity R_e cases cases/100k daily cases
Green WI 39 1 2.2 144 390 4
Door WI 47 2 2.2 103 380 3
Kewaunee WI 42 3 1.8 124 610 3
Dodge WI 13 4 1.4 779 890 18
Pierce WI 35 5 1.6 201 480 7
Milwaukee WI 1 6 0.9 20721 2170 212
Portage WI 23 7 1.6 386 550 8
Brown WI 3 12 1.1 4187 1610 40
Dane WI 2 13 1.1 4437 840 53
Racine WI 5 14 1.1 3443 1760 47
Kenosha WI 6 20 1.1 2638 1570 34
Waukesha WI 4 21 0.9 4026 1010 80
Outagamie WI 9 22 1.1 1205 650 22
Walworth WI 8 37 0.7 1316 1280 16
Rock WI 7 39 0.8 1543 950 9

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
McLeod MN 35 1 2.2 163 450 7
Isanti MN 46 2 1.8 120 310 3
Hennepin MN 1 3 1.0 18828 1520 213
Ramsey MN 2 4 1.0 7318 1350 93
St. Louis MN 18 5 1.3 508 250 19
Dakota MN 3 6 1.0 4257 1020 68
Scott MN 9 7 1.1 1506 1050 30
Anoka MN 4 8 1.0 3557 1020 48
Washington MN 6 10 1.0 2041 810 32
Olmsted MN 8 11 1.1 1697 1110 17
Stearns MN 5 14 0.9 2877 1830 12
Nobles MN 7 36 0.8 1756 8040 2
SD
county ST case rank severity R_e cases cases/100k daily cases
Hughes SD 16 1 2.1 88 500 1
Meade SD 17 2 1.7 87 320 3
Charles Mix SD 12 3 2.0 102 1090 0
Yankton SD 11 4 1.6 108 480 1
Minnehaha SD 1 5 1.0 4351 2330 28
Pennington SD 2 6 1.2 876 800 9
Brookings SD 7 7 1.5 129 380 2
Codington SD 8 8 1.5 128 460 2
Brown SD 5 9 1.1 428 1100 4
Lincoln SD 3 10 0.8 613 1120 9
Union SD 6 11 0.9 211 1390 3
Clay SD 9 12 1.0 125 900 2
Beadle SD 4 17 0.0 588 3200 0
ND
county ST case rank severity R_e cases cases/100k daily cases
Morton ND 4 1 1.3 326 1070 11
Stark ND 6 2 1.3 247 800 10
Burleigh ND 2 3 1.1 1109 1180 31
Williams ND 5 4 1.2 260 760 6
Cass ND 1 5 0.9 2998 1720 16
Grand Forks ND 3 6 1.0 663 940 7
Ward ND 7 7 1.0 211 310 6
Benson ND 8 9 0.8 138 2000 6
Stutsman ND 9 10 0.9 128 610 3

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
New Haven CT 2 1 0.8 13150 1530 17
Middlesex CT 6 2 1.2 1399 860 3
Windham CT 8 3 1.0 731 630 6
New London CT 5 4 1.0 1437 530 5
Fairfield CT 1 5 0.6 17968 1900 26
Hartford CT 3 6 0.6 12779 1430 21
Tolland CT 7 7 0.7 1065 700 6
Litchfield CT 4 8 0.5 1611 880 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.1 21714 2740 77
Essex MA 3 2 1.1 17695 2270 67
Middlesex MA 1 3 1.0 26255 1650 78
Norfolk MA 5 4 1.0 10572 1510 47
Bristol MA 6 5 1.0 9310 1670 35
Plymouth MA 7 6 1.0 9216 1800 19
Worcester MA 4 7 0.8 13568 1650 33
Hampden MA 8 8 0.9 7563 1610 21
Barnstable MA 9 10 0.8 1793 840 7
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.3 15064 2370 100
Kent RI 2 2 1.4 1497 910 15
Newport RI 4 3 1.5 398 480 3
Washington RI 3 4 1.4 608 480 3
Bristol RI 5 5 1.2 318 650 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Allegany NY 51 1 2.6 79 170 1
New York City NY 1 2 1.0 231893 2750 302
Niagara NY 15 3 1.5 1483 700 8
Suffolk NY 2 4 1.0 43684 2940 68
Nassau NY 3 5 1.0 43587 3210 53
Montgomery NY 40 6 1.6 170 340 3
Livingston NY 39 7 1.7 175 270 2
Erie NY 7 8 1.1 8789 960 43
Monroe NY 8 10 1.1 4900 660 27
Rockland NY 5 11 1.3 13923 4300 9
Dutchess NY 9 12 1.1 4596 1560 15
Westchester NY 4 13 0.9 36136 3730 28
Orange NY 6 29 0.7 11149 2950 8

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Windham VT 3 1 2.7 103 240 0
Rutland VT 4 2 1.8 98 170 2
Chittenden VT 1 3 0.7 726 450 1
Bennington VT 5 4 0.6 86 240 0
Franklin VT 2 5 0.5 119 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Cumberland ME 1 1 0.8 2077 710 5
York ME 2 2 0.7 671 330 3
Penobscot ME 5 3 0.9 154 100 1
Androscoggin ME 3 4 0.7 555 520 1
Kennebec ME 4 5 0.6 172 140 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Strafford NH 4 1 1.4 348 270 3
Rockingham NH 2 2 1.1 1677 550 8
Belknap NH 5 3 1.3 116 190 2
Hillsborough NH 1 4 0.8 3825 930 12
Cheshire NH 7 5 1.1 98 130 1
Merrimack NH 3 6 0.9 463 310 1
Carroll NH 8 7 0.8 94 200 1
Grafton NH 6 8 0.0 104 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 8592 2100 130
Aiken SC 16 2 1.2 1816 1090 53
Spartanburg SC 8 3 1.1 4006 1330 54
York SC 9 4 1.1 3468 1340 58
Calhoun SC 41 5 1.4 379 2580 12
Greenville SC 2 6 0.9 10747 2160 107
Florence SC 10 7 1.0 3340 2410 68
Beaufort SC 7 8 0.9 4008 2190 78
Charleston SC 1 10 0.8 12144 3080 108
Horry SC 4 13 0.9 8474 2640 74
Berkeley SC 6 22 0.9 4098 1960 47
Lexington SC 5 25 0.8 4896 1710 49
NC
county ST case rank severity R_e cases cases/100k daily cases
Alleghany NC 80 1 1.9 189 1720 18
Stanly NC 37 2 1.4 1013 1660 26
Mecklenburg NC 1 3 0.9 21921 2080 207
Wake NC 2 4 0.9 11838 1130 142
Haywood NC 61 5 1.3 398 660 18
Wilkes NC 43 6 1.4 771 1130 9
Gaston NC 6 7 1.1 3254 1500 52
Union NC 8 10 1.0 3024 1330 46
Cumberland NC 9 13 1.0 2996 900 54
Guilford NC 4 15 0.9 5497 1050 60
Forsyth NC 5 19 0.9 5142 1380 46
Durham NC 3 22 0.9 6087 1990 43
Johnston NC 7 26 0.8 3248 1700 42

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Flathead MT 5 1 1.3 315 320 13
Missoula MT 4 2 1.3 317 270 11
Yellowstone MT 1 3 1.0 1217 770 29
Silver Bow MT 9 4 1.2 84 240 4
Big Horn MT 3 5 0.9 410 3070 15
Lewis and Clark MT 8 6 1.1 156 230 5
Gallatin MT 2 7 0.8 934 890 12
Cascade MT 7 9 0.7 166 200 3
Lake MT 6 10 0.7 179 600 2
WY
county ST case rank severity R_e cases cases/100k daily cases
Park WY 7 1 1.7 132 450 4
Carbon WY 10 2 1.2 96 620 3
Fremont WY 1 3 1.1 504 1260 5
Lincoln WY 9 4 1.3 101 530 2
Laramie WY 2 5 0.8 501 510 6
Uinta WY 4 6 0.8 276 1340 3
Natrona WY 6 7 0.8 231 290 2
Teton WY 3 8 0.5 375 1630 4
Sweetwater WY 5 9 0.6 258 580 2
Campbell WY 8 10 0.5 122 260 1
ID
county ST case rank severity R_e cases cases/100k daily cases
Canyon ID 2 1 1.2 5562 2620 148
Teton ID 24 2 1.9 80 720 4
Bonneville ID 5 3 1.3 955 850 47
Ada ID 1 4 1.0 8700 1950 136
Bannock ID 10 5 1.3 422 500 13
Madison ID 20 6 1.4 154 400 4
Nez Perce ID 21 7 1.5 144 360 2
Kootenai ID 3 9 1.0 1764 1150 37
Jerome ID 9 13 1.2 468 2000 8
Twin Falls ID 4 16 1.0 1337 1600 22
Cassia ID 7 18 1.0 520 2200 8
Minidoka ID 8 20 0.9 475 2300 7
Blaine ID 6 22 1.0 577 2620 1

## Warning in FUN(X[[i]], ...): NaNs produced

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Belmont OH 29 1 2.4 603 880 3
Darke OH 38 2 1.7 376 730 12
Champaign OH 59 3 1.7 168 430 11
Lawrence OH 45 4 1.6 257 420 10
Richland OH 30 5 1.5 592 490 13
Franklin OH 1 6 0.9 18006 1410 182
Cuyahoga OH 2 7 1.0 13278 1060 130
Hamilton OH 3 12 0.9 9477 1170 77
Summit OH 6 13 1.0 3487 640 47
Lucas OH 4 15 0.8 5262 1220 82
Butler OH 8 18 1.0 2860 760 36
Mahoning OH 9 20 1.0 2502 1080 20
Marion OH 7 25 1.1 2920 4470 9
Montgomery OH 5 34 0.7 4235 800 44
IL
county ST case rank severity R_e cases cases/100k daily cases
Jersey IL 59 1 2.7 92 420 6
Jefferson IL 36 2 2.0 235 620 12
LaSalle IL 19 3 1.8 680 620 36
Woodford IL 51 4 2.0 137 350 9
Cook IL 1 5 1.1 109849 2100 663
Tazewell IL 23 6 1.6 480 360 25
Will IL 5 7 1.3 9088 1320 102
DuPage IL 3 8 1.2 12019 1290 112
Kane IL 4 9 1.2 9666 1820 83
Lake IL 2 11 1.1 12488 1770 94
St. Clair IL 6 12 1.1 4290 1630 75
Madison IL 9 14 1.1 2457 920 59
McHenry IL 8 20 1.1 3170 1030 41
Winnebago IL 7 47 0.8 3763 1310 17
IN
county ST case rank severity R_e cases cases/100k daily cases
Putnam IN 43 1 2.7 280 750 13
Sullivan IN 75 2 2.6 102 490 6
Clinton IN 36 3 2.0 409 1270 12
Carroll IN 59 4 2.1 170 850 7
Wells IN 58 5 1.9 176 630 6
Spencer IN 70 6 2.0 129 630 3
Vigo IN 30 7 1.5 580 540 25
Lake IN 2 8 1.2 7446 1530 77
Marion IN 1 9 1.1 15638 1660 160
St. Joseph IN 5 12 1.1 3369 1250 53
Hendricks IN 8 17 1.2 1856 1150 19
Allen IN 4 18 1.1 3805 1030 38
Vanderburgh IN 7 21 1.0 1889 1040 39
Elkhart IN 3 23 1.0 4842 2380 36
Hamilton IN 6 24 1.0 2688 850 37
Cass IN 9 50 1.0 1775 4660 5

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
White TN 63 1 2.2 261 980 16
Lake TN 25 2 2.2 815 10830 14
Johnson TN 64 3 1.8 260 1460 26
Weakley TN 53 4 1.7 395 1170 32
Overton TN 74 5 1.9 165 750 8
Smith TN 48 6 1.5 439 2260 18
Benton TN 78 7 1.6 139 860 11
Shelby TN 1 8 0.9 23096 2460 317
Knox TN 5 16 0.9 4657 1020 109
Davidson TN 2 17 0.8 22458 3280 181
Wilson TN 8 19 1.1 2222 1670 34
Williamson TN 6 22 1.0 3455 1580 47
Sumner TN 7 24 1.0 3354 1870 40
Hamilton TN 4 27 0.8 5959 1670 70
Rutherford TN 3 28 0.8 6400 2080 74
Montgomery TN 9 31 0.9 1871 950 36
KY
county ST case rank severity R_e cases cases/100k daily cases
Nelson KY 35 1 1.9 219 480 6
Adair KY 31 2 1.9 229 1190 5
Fayette KY 2 3 1.2 3724 1170 88
Jefferson KY 1 4 1.1 7866 1030 155
Henry KY 54 5 1.7 117 740 6
Madison KY 14 6 1.3 457 510 15
Washington KY 71 7 1.6 76 630 3
Warren KY 3 16 1.0 2588 2050 32
Boone KY 5 27 1.0 1079 840 13
Kenton KY 4 35 0.8 1388 840 14
Shelby KY 7 37 1.0 740 1580 7
Daviess KY 6 41 0.9 752 750 8
Christian KY 9 43 0.8 627 870 10
Muhlenberg KY 8 59 0.8 641 2060 2

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Stone MO 51 1 1.8 121 380 8
Pike MO 55 2 1.8 92 500 5
Christian MO 25 3 1.5 348 410 15
St. Louis MO 1 4 0.9 14632 1470 267
Jefferson MO 5 5 1.1 1736 780 52
St. Louis city MO 2 6 1.0 5062 1630 93
Adair MO 44 7 1.6 152 600 4
Jackson MO 4 8 1.0 3930 570 98
St. Charles MO 3 13 0.9 4049 1040 76
Greene MO 6 17 1.0 1432 500 33
Boone MO 7 26 0.9 1351 770 23
Buchanan MO 9 47 0.7 1078 1210 4
Jasper MO 8 51 0.5 1240 1040 7
AR
county ST case rank severity R_e cases cases/100k daily cases
Clay AR 52 1 2.9 124 820 4
Poinsett AR 35 2 2.1 228 950 17
Logan AR 33 3 1.9 240 1100 16
Cleveland AR 57 4 1.9 92 1120 5
Pulaski AR 2 5 1.2 5349 1360 103
Chicot AR 19 6 1.4 734 6780 44
Desha AR 43 7 1.7 187 1570 8
Craighead AR 9 8 1.4 1290 1220 38
Sebastian AR 4 11 1.2 2100 1650 65
Crittenden AR 7 14 1.2 1336 2730 24
Jefferson AR 6 20 1.0 1472 2090 26
Washington AR 1 23 0.9 6245 2730 45
Benton AR 3 29 0.8 4707 1820 34
Pope AR 8 33 0.8 1293 2030 18
Hot Spring AR 5 34 1.0 1497 4470 8

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1584.8 seconds to compute.
2020-08-08 08:01:35

version history

Today is 2020-08-08.
80 days ago: Multiple states.
72 days ago: \(R_e\) computation.
69 days ago: created color coding for \(R_e\) plots.
64 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
64 days ago: “persistence” time evolution.
57 days ago: “In control” mapping.
57 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
49 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
44 days ago: Added Per Capita US Map.
42 days ago: Deprecated national map.
38 days ago: added state “Hot 10” analysis.
33 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
31 days ago: added per capita disease and mortaility to state-level analysis.
19 days ago: changed to county boundaries on national map for per capita disease.
14 days ago: corrected factor of two error in death trend data.
10 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
5 days ago: added county level “baseline control” and \(R-e\) maps.
1 days ago: fixed normalization error on total disease stats plot.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.